In the insurance industry and in other fields in which risk is assessed (including such diverse fields as medical treatment, financial modeling and portfolio management, and environmental impact regulation), it is known to develop and use a risk-assessment model of a population. The risk-assessment model provides a technique for determining which population members are more subject or less subject to particular risks (or to an aggregate of risks) than the norm for that population. For example, in life insurance underwriting, it is known to evaluate past and present medical data so as to determine what insurance premium the underwriter wishes to charge.
While these known methods generally achieve the goal of assessing risk for particular individuals in comparison to a population norm, they have the drawback of making a risk assessment that is fixed at a particular point in time. That is, these risk-assessment models rely on static data, in particular (1) static data about the individual population member, (2) static data about the population norm, and (3) static data about risks associated or correlated with the data about the individual population member. However, risk for individual population members depends not only on their present data, but also on their future data, including both data about behavior and environment.
A first type of problem for the known art includes those individuals that have a progressive disease or degenerative condition, in which the disease or condition progresses at a rate that is responsive to behavior or environment of the individual. For such individuals, risk is more accurately evaluated as a function of behavior measured over time and environment measured over time, rather than as a static value that is a function only of present behavior and environment. For example, a first patient with diabetes can proceed with relatively small risk if that first patient is aware of and active in management of behavioral and environmental risk factors. In contrast, an otherwise identical second patient will have significantly greater risk if that second patient is either unaware of, or unable or unwilling to take charge of, behavioral and environmental risk factors.
Related to this first type of problem is the problem of determining trends for individual risk-assessment. For example, an individual with a history of diabetes may suffer a significant increase or decrease in effects thereof, due at least in part to that patient's actions with regard to behavioral and environmental risk factors. Similarly to the first type of problem, that individual will be rationally assessed a significantly greater or lesser risk than originally, if the new facts were known to the underwriter. Such trends may differ significantly from any trends that might have been discerned from past medical history alone; such trends may also themselves involve genetic, environmental, or behavioral components, or some combination thereof.
A second type of problem for the known art includes individuals whose risk-assessment significantly changes due to the vicissitudes of their life trajectory. This can include progression of a disease or condition, responsive at least in part to behavioral or environmental factors. For a more striking example, an individual may suffer a myocardial infarction, or become infected with an HIV variant. Similarly to the first type of problem, that individual would be rationally assessed a significantly greater risk than originally, if the new facts were known to the underwriter. Alternatively, an individual may be successfully treated for a “curable” disease such as Hodgkin's disease or some forms of cancer. Such vicissitudes of life trajectory may themselves involve genetic, environmental, or behavioral components, or some combination thereof.
A third type of problem for the known art includes individuals who significantly change their behavior or environment, particularly when those individuals are susceptible to the elements of their behavior or environment they change. For example, an individual with diabetes can determine to alter their diet favorably or unfavorably. For a more striking example, an individual may take up smoking or skydiving as habits. That individual will become a significantly greater risk than the underwriter originally assessed.
Moreover, new medical research may indicate risk factors that were not known at the time risk for the individual was originally assessed. These could include past medical information not known at the time to be important, tests available in the future for risk factors not known at the time at all, or changes in the medical history of the individual that place that individual in different risk factor categories. Such past medical information or risk factors may themselves involve genetic, environmental, or behavioral elements, or some combination thereof.
Accordingly, it would be advantageous to collect feedback from individual population members, whether on a periodic or aperiodic basis, and whether prompted by selected events or not. Such feedback would allow underwriters or other risk-assessment or risk-management personnel to determine specific risk-related information about each individual population member, and to adjust (such as to make more accurate or precise) insurance models and risk-assessment models to fit the new data. Such feedback enables the advantage of providing information about the time-varying nature of individual measures which can be used in the dynamic risk assessment model presented in the present invention. For instance, a weight gain of 10 pounds per year, an increase in diastolic blood pressure of 10 points per year, and an increase of cholesterol of 10 points per year could be tracked over time and would yield health risk information.
To achieve this advantage, a first aspect of the invention is that feedback is collected by a client-server system in which data is requested or required from population members. A server device, responsive to a risk-assessment model, prompts a client device supplied to population members to request information from population members, in order to determine whether aggregate measures or individual measures of risk-assessment remain in coherence with the model. The client device collects the data and supplies it to the server device, which can, in response to dynamically collected data, adjust the model, adjust risk assessments for selected population members (or groups thereof), or determine further information to collect from population members.
Upon achieving this advantage, a second aspect of the invention is to provide a set of superior risk-assessment models and insurance models in response to the feedback. These superior risk-assessment models and insurance models can include information about the risk-related behavior, risk-related trends, or forward-looking risk-assessment of selected individuals or selected subsets of the population. These superior risk-assessment models and insurance models can be responsive to data-mining techniques described in related patent applications, described below, hereby incorporated by reference as if fully set forth herein. These superior risk-assessment models can also incorporate known scientific information regarding health risk or disease progression, such as well-determined correlations of risk factors and disease incidence or progression from large research studies, or well-known shape of 5-year survival curves for patients having specific types of cancer.
Accordingly, it would also be advantageous to provide a set of techniques for modeling and scoring risk-assessment and a set of insurance products derived therefrom, using dynamic assessment of risk indicators and associated consequences for a population. This advantage is achieved in an embodiment of the invention in which a population (such as a population of medical patients) is assessed both at a selected time and afterward for those risk indicators and for consequences associated therewith. A client-server system provides dynamic data collection and analysis, dynamic risk assessment in response to that data collection and analysis, and dynamic treatment options and utilization review for each population member.